Search Results for author:
Found 6 papers, 2 papers with code
Sequence Alignment Ensemble with a Single Neural Network for Sequence Labeling
In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework.
Ranked #1 on
Part-Of-Speech Tagging
on Penn Treebank
Sequential Alignment Methods for Ensemble Part-of-Speech Tagging
Herein, we present a weighted ensemble technique using a sequence alignment approach for a Part-of-speech tagger.
Ranked #1000000000 on
Part-Of-Speech Tagging
on Penn Treebank
Impact of representation matching with neural machine translation
Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models.
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System
To our best knowledge, our work is the first comprehensive study of a modularized E2E multi-domain dialog system that learning from each component to the entire dialog policy for task success.
Learning to Embed Semantic Correspondence for Natural Language Understanding
While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text.
Concept Equalization to Guide Correct Training of Neural Machine Translation
Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences.
